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Stop Losses

import numpy as np
#From strategy library
class BetaAlgorithm(QCAlgorithm):

def Initialize(self):
self.SetStartDate(2016, 1, 1) # Set Start Date
self.SetEndDate(2017, 1, 1) # Set End Date
self.SetCash(10000) # Set Strategy Cash

# Dow 30 companies.
self.symbols = [self.AddEquity(ticker).Symbol
for ticker in ['AAPL', 'AXP', 'BA', 'CAT', 'CSCO', 'CVX', 'DD',
'DIS', 'GE', 'GS', 'HD', 'IBM', 'INTC', 'JPM',
'KO', 'MCD', 'MMM', 'MRK', 'MSFT', 'NKE', 'PFE',
'PG', 'TRV', 'UNH', 'UTX', 'V', 'VZ', 'WMT', 'XOM'] ]

# Benchmark
self.benchmark = Symbol.Create('SPY', SecurityType.Equity, Market.USA)

# Set number days to trace back
self.lookback = 21

# Schedule Event: trigger the event at the begining of each month.
self.Schedule.On(self.DateRules.MonthStart(self.symbols[0]),
self.TimeRules.AfterMarketOpen(self.symbols[0]),
self.Rebalance)


def Rebalance(self):

# Fetch the historical data to perform the linear regression
history = self.History(
self.symbols + [self.benchmark],
self.lookback,
Resolution.Daily).close.unstack(level=0)

symbols = self.SelectSymbols(history)

# Liquidate positions that are not held by selected symbols
for holdings in self.Portfolio.Values:
symbol = holdings.Symbol
if symbol not in symbols and holdings.Invested:
self.Liquidate(symbol)

# Invest 100% in the selected symbols
for symbol in symbols:
self.SetHoldings(symbol, 1)


def SelectSymbols(self, history):
'''Select symbols with the highest intercept/alpha to the benchmark
'''
alphas = dict()

# Get the benchmark returns
benchmark = history[self.benchmark].pct_change().dropna()

# Conducts linear regression for each symbol and save the intercept/alpha
for symbol in self.symbols:

# Get the security returns
returns = history[symbol].pct_change().dropna()
returns = np.vstack([returns, np.ones(len(returns))]).T

# Simple linear regression function in Numpy
result = np.linalg.lstsq(returns, benchmark)
alphas[symbol] = result[0][1]

# Select symbols with the highest intercept/alpha to the benchmark
selected = sorted(alphas.items(), key=lambda x: x[1], reverse=True)[:2]
return [x[0] for x in selected]

Hello everyone, I am new to QuantConnect and I am having difficulty implementing stop losses for this algorithm I cloned from the strategy library. How can I put a normal stop loss and/or a trailing stop loss into the algorithm to limit losses? Thanks for the help in advance.

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Hi Darsh, 

You can use risk management such as a trailing stop loss. 

self.SetRiskManagement(TrailingStopRiskManagementModel(0.04))

Here are links to the documentation which I believe can help you: Risk Management Stop Market Order.

Let me know if this helps.

 

Jovad

1

This helps a lot, thank you!

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0

The material on this website is provided for informational purposes only and does not constitute an offer to sell, a solicitation to buy, or a recommendation or endorsement for any security or strategy, nor does it constitute an offer to provide investment advisory services by QuantConnect. In addition, the material offers no opinion with respect to the suitability of any security or specific investment. QuantConnect makes no guarantees as to the accuracy or completeness of the views expressed in the website. The views are subject to change, and may have become unreliable for various reasons, including changes in market conditions or economic circumstances. All investments involve risk, including loss of principal. You should consult with an investment professional before making any investment decisions.


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